Update app.py

app.py

@@ -1,3 +1,75 @@
 import gradio as gr
+import requests
+import json
+import os
 
-gr.Interface.load("models/EleutherAI/gpt-j-6B").launch()
+#Import Hugging Face's Transformers
+from transformers import pipeline
+# This is to log our outputs in a nicer format
+from pprint import pprint
+
+# from transformers import GPTJForCausalLM
+# import torch
+
+# model = GPTJForCausalLM.from_pretrained(
+#     "EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16, low_cpu_mem_usage=True
+# )
+
+generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
+
+# from transformers import GPTJForCausalLM, AutoTokenizer
+# import torch
+
+# model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16, low_cpu_mem_usage=True)
+# tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
+
+# prompt = (
+#     "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
+#     "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
+#     "researchers was the fact that the unicorns spoke perfect English."
+# )
+
+# input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+# gen_tokens = model.generate(
+#     input_ids,
+#     do_sample=True,
+#     temperature=0.9,
+#     max_length=100,
+# )
+# gen_text = tokenizer.batch_decode(gen_tokens)[0]
+
+def run(prompt, max_len, temp):
+    min_len = 1
+    output = generator(prompt, do_sample=True, min_length=min_len, max_length=max_len, temperature=temp)
+    return (output[0]['generated_text'],"")
+    
+if __name__ == "__main__":
+    demo = gr.Blocks()
+    with demo:
+        with gr.Row():
+            gr.Markdown(value=description)
+        with gr.Row():
+            with gr.Column():
+                text = gr.Textbox(
+                    label="Input",
+                    value=" ",  # should be set to " " when plugged into a real API
+                )
+                tokens = gr.Slider(1, 250, value=50, step=1, label="Tokens to generate")
+                temp = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature")
+
+                with gr.Row():
+                    submit = gr.Button("Submit")
+            with gr.Column():
+                text_error = gr.Markdown(label="Log information")
+                text_out = gr.Textbox(label="Output")
+        with gr.Row():
+        submit.click(
+            run,
+            inputs=[text, tokens, temperature],
+            outputs=[text_out, text_error],
+        )
+
+    demo.launch()
+
+#gr.Interface.load("models/EleutherAI/gpt-j-6B").launch()